Remote sensing is the science of acquiring information about the earth's land and water resources without coming into physical contact with the feature to be studied. One of three basic outcomes can effect light (electromagnetic energy) as it passes through the earth's atmosphere and strikes an object; it can be absorbed, reflected or transmitted. In general, remote sensing measures that part of the electromagnetic spectrum that is either reflected or emitted (thermal energy) from an object. As an object (green plant) grows, generally, the leaf area of the plant increases, and the different portions of the electromagnetic spectrum respond accordingly (i.e., red reflectance decreases and near-infrared reflectance increases).
There are different methods of data collection from remote sensing systems; a single band (panchromatic), several bands (multi spectral) or hundreds of bands (hyperspectral). These images of reflectance can be useful at a specific wavelength or waveband, but are often more useful when combined with images at other wavelengths (i.e., multispectral or hyperspectral). Multiple wavelength reflectance data allows for the creation of field maps that illustrate ratios of selected wavelengths. These mathematical ratios of wavebands (a type of vegetation indices) have statistically significant relationships with vegetative conditions with an area, and when collected strategically over time are useful in visualizing crop growth and development change over the course of a growing season (temporal resolution).
Changes in reflectance values over time can be attributed to differences in plant growth and development or plant health. This assumes that environmental conditions that may effect the reflectance of light have remained the same over time. However, we know that it is unlikely that the sun will be at the exact same angle, that cloud patterns are the same, that particulate matter in the atmosphere will be constant or the position of the sensor over the object will be unchanged from one date of image capture to the next. These factors introduce variation between data sets not attributable to the growing crop, thus making it virtually impossible to accumulate data over a growing season (growing season is considered from the end of harvest through the next harvest) that can be compared to identify changes in the crop alone. While there have been various prior art attempts to eliminate these kinds of unwanted variation, (i.e., using laser light sources at night instead of the sun as a light source, schemes for adjusting the variation in photographic film, and others) the inventors are not aware of successful methodology that has been developed for taking the data as collected and then satisfactorily adjusting the data itself for comparison over time (i.e., through a growing season). As reliable data comparisons have not been made in the prior art, there are few reliable conclusions that can be drawn for a grower to help him in making the few decisions that are within his power to decide.
To solve these and other problems in the prior art, the inventors herein have succeeded in developing a methodology for normalizing data taken at different times over a growing season which eliminates the effect of the changing environmental and other conditions on the data so that the data is truly representative of the changing, growing crop in the field. This methodology can be applied to data in any form, but the inventors have chosen to apply it to visible and infrared reflectance data that have been converted to a form of vegetative index, such as the Normalized Difference Vegetative Index (NDVI). There are advantages to converting reflectance data to an NDVI, as is explained in greater detail below. Once converted, the data is then normalized using a statistical analysis over each data set independently of the other data. This is done by subtracting the mean value from each pixel value and then dividing the result by the standard deviation. By normalizing each data set, the extraneous variations introduced into the data is removed and the data may then be compared to gain insight about the crop and field. The power of this normalization should not be underestimated. It allows for the first time, as known to the inventors, agricultural data taken at different times and necessarily under different environmental conditions to be compared and to be combined as a tool for further analysis. This powerfully eliminates the effects of varying influences by factoring them out of the data while the prior art has either rather ineffectively sought to control the conditions under which the data were collected or to control the environmental conditions subject to control and ignore all others.
Still another aspect to the present invention is the temporal comparison of this normalized data which provides for the first time information that a grower may find useful in his decision making process. The inventors have found that the data is useful to define different segments of the field that perform similarly for growing crop and to create a story which characterizes the history of a growing season as it unfolds in these differently defined segments of the field. These “stories” for different parts of a field can be quite unique and yet produce very similar crop yield. Taking yield alone, a grower would see no difference between these different field areas, and previously would have been led to believe that he should make the same decisions for them, and as a result not achieve any improvement in yield. For example, one area might experience an early decline in vegetation, perhaps caused by too much moisture which depresses its final yield. Another area may be dry which also depresses its final yield. Yet the yield value alone would not distinguish between them. With the present invention, it is finally possible to create these “stories” or “histories” for the individually defined “pixels” of an entire field, and then to associate these pixels with field areas that share the same story, which enables the field to be divided into “like story” areas, or crop response zones as the inventors have defined the term. These crop response zones are areas of a field that have similar vegetative values at the time intervals in which the data is taken. So, for example, one such crop response zone might have low vegetation at the first and second intervals, mid level vegetation at the third interval, and high level vegetation at the last interval or end of the growing season. Another crop response zones might have high level vegetation at all intervals. Still other crop response zones would have other patterns of vegetation.
Crop response zones represent segments of the field where the crop grew similarly over time in response to certain static (soil texture, organic matter, elevation, slope) and dynamic variables (precipitation, solar radiation, air temperature). Thus, an understanding of the relationships between static and dynamic variables and resultant crop response will enable the grower to prescribe and apply certain combinations of controllable inputs such as seed, tillage, fertilizers and pesticides uniquely for specific field segments. For example, a grower will be able to identify those fields or segments of fields which respond positively to a certain hybrid/variety of seed. The inventors have utilized mathematical analysis to more rigorously define these crop response zones and that more rigorous analysis is explained below. However, an important part of the invention is that a grower can now define segments of his field that share common characteristics for which specifically tailored decisions may be made to optimize the yield across the entire field. Previously, growers were not provided with any scientifically valid way to define these field segments, even though many growers were able to adjust their decision making based on their great skill and experience over many years with their own fields. While the innate good “feel” that a grower commonly uses may result in some yield improvement, the present invention will now, for the first time, provide some validation to the grower that specific field areas exhibit certain characteristics that require different decisions in order to maximize their yield.
While some of the advantages and features of the present invention have been described above, a greater understanding of the invention may be attained by referring to the drawings and detailed description of the preferred embodiment which follows.